High-throughput Histopathological Image Analysis Via Robust Cell Segmentation and Hashing

Overview

Journal Med Image Anal

Publisher Elsevier

Specialty Radiology

Date 2015 Nov 25

PMID 26599156

Citations 17

Authors

Xiaofan Zhang

Fuyong Xing

Hai Su

Lin Yang

Shaoting Zhang

Affiliations

Soon will be listed here.

Abstract

Computer-aided diagnosis of histopathological images usually requires to examine all cells for accurate diagnosis. Traditional computational methods may have efficiency issues when performing cell-level analysis. In this paper, we propose a robust and scalable solution to enable such analysis in a real-time fashion. Specifically, a robust segmentation method is developed to delineate cells accurately using Gaussian-based hierarchical voting and repulsive balloon model. A large-scale image retrieval approach is also designed to examine and classify each cell of a testing image by comparing it with a massive database, e.g., half-million cells extracted from the training dataset. We evaluate this proposed framework on a challenging and important clinical use case, i.e., differentiation of two types of lung cancers (the adenocarcinoma and squamous carcinoma), using thousands of lung microscopic tissue images extracted from hundreds of patients. Our method has achieved promising accuracy and running time by searching among half-million cells .

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References

Grala B, Markiewicz T, Kozlowski W, Osowski S, Slodkowska J, Papierz W . New automated image analysis method for the assessment of Ki-67 labeling index in meningiomas. Folia Histochem Cytobiol. 2010; 47(4):587-92. DOI: 10.2478/v10042-008-0098-0. View

Markiewicz T, Wisniewski P, Osowski S, Patera J, Kozlowski W, Koktysz R . Comparative analysis of methods for accurate recognition of cells through nuclei staining of Ki-67 in neuroblastoma and estrogen/progesterone status staining in breast cancer. Anal Quant Cytol Histol. 2009; 31(1):49-62. View

Mao K, Zhao P, Tan P . Supervised learning-based cell image segmentation for p53 immunohistochemistry. IEEE Trans Biomed Eng. 2006; 53(6):1153-63. DOI: 10.1109/TBME.2006.873538. View

Ali S, Veltri R, Epstein J, Christudass C, Madabhushi A . Adaptive energy selective active contour with shape priors for nuclear segmentation and gleason grading of prostate cancer. Med Image Comput Comput Assist Interv. 2011; 14(Pt 1):661-9. DOI: 10.1007/978-3-642-23623-5_83. View

Qi X, Xing F, Foran D, Yang L . Robust segmentation of overlapping cells in histopathology specimens using parallel seed detection and repulsive level set. IEEE Trans Biomed Eng. 2011; 59(3):754-65. PMC: 3655778. DOI: 10.1109/TBME.2011.2179298. View

Edwards S, Roberts C, McKean M, Cockburn J, Jeffrey R, Kerr K . Preoperative histological classification of primary lung cancer: accuracy of diagnosis and use of the non-small cell category. J Clin Pathol. 2000; 53(7):537-40. PMC: 1731233. DOI: 10.1136/jcp.53.7.537. View

Lin G, Adiga U, Olson K, Guzowski J, Barnes C, Roysam B . A hybrid 3D watershed algorithm incorporating gradient cues and object models for automatic segmentation of nuclei in confocal image stacks. Cytometry A. 2003; 56(1):23-36. DOI: 10.1002/cyto.a.10079. View

Monaco J, Hipp J, Lucas D, Smith S, Balis U, Madabhushi A . Image segmentation with implicit color standardization using spatially constrained expectation maximization: detection of nuclei. Med Image Comput Comput Assist Interv. 2013; 15(Pt 1):365-72. DOI: 10.1007/978-3-642-33415-3_45. View

Xu C, Prince J . Snakes, shapes, and gradient vector flow. IEEE Trans Image Process. 2008; 7(3):359-69. DOI: 10.1109/83.661186. View

10.

Wang J, Kumar S, Chang S . Semi-supervised hashing for large-scale search. IEEE Trans Pattern Anal Mach Intell. 2012; 34(12):2393-406. DOI: 10.1109/TPAMI.2012.48. View

11.

Park C, Huang J, Ji J, Ding Y . Segmentation, Inference and Classification of Partially Overlapping Nanoparticles. IEEE Trans Pattern Anal Mach Intell. 2012; 35(3):669-81. DOI: 10.1109/TPAMI.2012.163. View

12.

Ozolek J, Tosun A, Wang W, Chen C, Kolouri S, Basu S . Accurate diagnosis of thyroid follicular lesions from nuclear morphology using supervised learning. Med Image Anal. 2014; 18(5):772-80. PMC: 4084938. DOI: 10.1016/j.media.2014.04.004. View

13.

Zhang X, Liu W, Dundar M, Badve S, Zhang S . Towards large-scale histopathological image analysis: hashing-based image retrieval. IEEE Trans Med Imaging. 2014; 34(2):496-506. DOI: 10.1109/TMI.2014.2361481. View

14.

Loukas C, Wilson G, Vojnovic B, Linney A . An image analysis-based approach for automated counting of cancer cell nuclei in tissue sections. Cytometry A. 2003; 55(1):30-42. DOI: 10.1002/cyto.a.10060. View

15.

Jung C, Kim C . Segmenting clustered nuclei using H-minima transform-based marker extraction and contour parameterization. IEEE Trans Biomed Eng. 2010; 57(10):2600-4. DOI: 10.1109/TBME.2010.2060336. View

16.

Yu W, Lee H, Hariharan S, Bu W, Ahmed S . Evolving generalized Voronoi diagrams for accurate cellular image segmentation. Cytometry A. 2010; 77(4):379-86. DOI: 10.1002/cyto.a.20876. View

17.

El-Naqa I, Yang Y, Galatsanos N, Nishikawa R, Wernick M . A similarity learning approach to content-based image retrieval: application to digital mammography. IEEE Trans Med Imaging. 2004; 23(10):1233-44. DOI: 10.1109/TMI.2004.834601. View

18.

Janowczyk A, Chandran S, Singh R, Sasaroli D, Coukos G, Feldman M . High-throughput biomarker segmentation on ovarian cancer tissue microarrays via hierarchical normalized cuts. IEEE Trans Biomed Eng. 2011; 59(5):1240-52. DOI: 10.1109/TBME.2011.2179546. View

19.

Chang H, Han J, Spellman P, Parvin B . Multireference level set for the characterization of nuclear morphology in glioblastoma multiforme. IEEE Trans Biomed Eng. 2012; 59(12):3460-7. PMC: 3728286. DOI: 10.1109/TBME.2012.2218107. View

20.

Kolmogorov V, Zabih R . What energy functions can be minimized via graph cuts?. IEEE Trans Pattern Anal Mach Intell. 2004; 26(2):147-59. DOI: 10.1109/TPAMI.2004.1262177. View